# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from .llama import *
from ._utils import __version__
from unsloth_zoo.utils import _get_dtype, Version
from unsloth_zoo.hf_utils import dtype_from_config
from ..utils.packing import get_packed_info_from_kwargs
from ..utils.attention_dispatch import (
    AttentionConfig,
    AttentionContext,
    run_attention,
    select_attention_backend,
    SDPA,
)
from .gemma import (
    GemmaFixedRotaryEmbedding,
    GemmaFixedLinearScalingRotaryEmbedding,
    fast_geglu_inference,
)

try:
    from transformers.models.gemma2.modeling_gemma2 import (
        Gemma2Attention,
        Gemma2DecoderLayer,
        Gemma2Model,
        Gemma2ForCausalLM,
        Gemma2RotaryEmbedding,
        apply_rotary_pos_emb,
        repeat_kv,
    )
except:
    transformers_version = Version(transformers_version)
    if not transformers_version >= Version("4.42"):
        raise ImportError(
            f"Unsloth: Your transformers version of {transformers_version} does not support Gemma2.\n"
            f"The minimum required version is 4.42.3.\n"
            f'Try `pip install --upgrade "transformers>=4.42.3"`\n'
            f"to obtain the latest transformers build, then restart this session."
        )

from transformers.modeling_attn_mask_utils import (
    _prepare_4d_causal_attention_mask_for_sdpa,
)

# For Pytorch 2.1.1
try:
    from transformers.models.gemma2.modeling_gemma2 import (
        Gemma2SdpaAttention,
        Gemma2FlashAttention2,
    )
except:
    Gemma2SdpaAttention = Gemma2Attention
    Gemma2FlashAttention2 = Gemma2Attention

if HAS_FLASH_ATTENTION_SOFTCAPPING:
    from flash_attn import flash_attn_func


# Logit softcapping
def Gemma2Attention_fast_forward(
    self,
    hidden_states: torch.Tensor,
    causal_mask: Optional[BlockDiagonalCausalMask] = None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
    padding_mask: Optional[torch.LongTensor] = None,
    *args,
    **kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    # Clear inference
    if hasattr(self, "paged_attention"):
        del self.paged_attention_K
        del self.paged_attention_V
        del self.paged_attention
        del self.temp_QA
        del self.temp_KV
        del self.RH_Q
        del self.attention

    bsz, q_len, _ = hidden_states.size()

    n_heads = self.config.num_attention_heads
    n_groups = self.num_key_value_groups
    n_kv_heads = self.config.num_key_value_heads
    head_dim = self.head_dim
    assert n_kv_heads * n_groups == n_heads

    Q, K, V = self.apply_qkv(self, hidden_states)
    Q = Q.view(bsz, q_len, n_heads, head_dim).transpose(1, 2)
    K = K.view(bsz, q_len, n_kv_heads, head_dim).transpose(1, 2)
    V = V.view(bsz, q_len, n_kv_heads, head_dim).transpose(1, 2)
    seq_info = get_packed_info_from_kwargs(kwargs, Q.device)

    kv_seq_len = K.shape[-2]
    if past_key_value is not None:
        kv_seq_len += past_key_value[0].shape[-2]

    device_index = Q.device.index
    cos = self.rotary_emb.multi_gpu_cos_cached[device_index]
    sin = self.rotary_emb.multi_gpu_sin_cached[device_index]

    rope_position_ids = position_ids if position_ids is not None else kwargs.get("position_ids")
    if rope_position_ids is not None:
        # Useful for LongRoPE
        cos_var, sin_var = self.rotary_emb.get_cached(kv_seq_len, device_index)
        Q, K = fast_rope_embedding(Q, K, cos_var, sin_var, rope_position_ids)
    else:
        Q, K = fast_rope_embedding(Q, K, cos, sin)

    if past_key_value is not None:
        K = torch.cat([past_key_value[0], K], dim = 2)
        V = torch.cat([past_key_value[1], V], dim = 2)
    past_key_value = (K, V) if use_cache else None

    # Only enable if the attention_mask is True
    use_sliding_window = kwargs.get("use_sliding_window")
    has_sliding_window = (
        use_sliding_window
        if use_sliding_window is not None
        else isinstance(causal_mask, bool) and causal_mask is True
    )

    use_flash = HAS_FLASH_ATTENTION_SOFTCAPPING and attention_mask is None

    if use_flash:
        window = (-1, -1)
        sliding_window = getattr(self.config, "sliding_window", None)
        if has_sliding_window:
            sliding_window = sliding_window if sliding_window is not None else kv_seq_len
            window = (-1, -1) if kv_seq_len <= sliding_window else (sliding_window, sliding_window)

        if not hasattr(self, "_flash_attention_softmax_scale"):
            self._flash_attention_softmax_scale = 1.0 / (self.config.query_pre_attn_scalar**0.5)

        use_varlen = seq_info is not None and past_key_value is None

        attention_config = AttentionConfig(
            backend = select_attention_backend(use_varlen),
            n_kv_heads = n_kv_heads,
            n_groups = n_groups,
            flash_dense_kwargs = {
                "causal": True,
                "softcap": self.config.attn_logit_softcapping,
                "softmax_scale": self._flash_attention_softmax_scale,
                "window_size": window,
            },
            flash_varlen_kwargs = {
                "dropout_p": 0.0,
                "softmax_scale": self._flash_attention_softmax_scale,
                "causal": True,
                "softcap": self.config.attn_logit_softcapping,
                "window_size": window,
            },
        )

        context = AttentionContext(
            bsz = bsz,
            q_len = q_len,
            kv_seq_len = kv_seq_len,
            n_heads = n_heads,
            head_dim = head_dim,
            requires_grad = hidden_states.requires_grad,
            seq_info = seq_info,
            attention_mask = attention_mask,
            causal_mask = causal_mask,
            sliding_window = sliding_window,
        )

        A = run_attention(config = attention_config, context = context, Q = Q, K = K, V = V)
        A = A.reshape(bsz, q_len, n_heads * head_dim)
    else:
        fx = (
            slow_inference_attention_softcapping
            if "_flag_for_generation" in kwargs
            else slow_attention_softcapping
        )
        A = fx(Q, K, V, causal_mask, self, bsz, kv_seq_len)
    A = self.apply_o(self, A)
    return A, None, past_key_value


# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L590
def Gemma2DecoderLayer_fast_forward(
    self,
    hidden_states: torch.Tensor,
    causal_mask: Optional[BlockDiagonalCausalMask] = None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: Optional[bool] = False,
    use_cache: Optional[bool] = False,
    padding_mask: Optional[torch.LongTensor] = None,
    *args,
    **kwargs,
):
    if use_cache and hasattr(self, "_flag_for_generation"):  # past_key_value is not None:
        out_weight = torch.empty(
            self.input_layernorm.weight.shape,
            dtype = torch.float32,
            device = f"{DEVICE_TYPE_TORCH}:0",
        )

        # Self Attention
        residual = hidden_states
        hidden_states = fast_rms_layernorm_inference_gemma(
            self.input_layernorm, hidden_states, out_weight
        )
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states = hidden_states,
            causal_mask = causal_mask,
            attention_mask = attention_mask,
            position_ids = position_ids,
            past_key_value = past_key_value,
            output_attentions = output_attentions,
            use_cache = use_cache,
            padding_mask = padding_mask,
            _flag_for_generation = self._flag_for_generation,
            **kwargs,
        )
        hidden_states = fast_rms_layernorm_inference_gemma(
            self.post_attention_layernorm, hidden_states, out_weight
        )
        hidden_states += residual

        # Fully Connected
        residual = hidden_states
        hidden_states = fast_rms_layernorm_inference_gemma(
            self.pre_feedforward_layernorm, hidden_states, out_weight
        )
        hidden_states = fast_geglu_inference(self.mlp, hidden_states)
        hidden_states = fast_rms_layernorm_inference_gemma(
            self.post_feedforward_layernorm, hidden_states, out_weight
        )
        hidden_states += residual
    else:
        residual = hidden_states
        hidden_states = fast_rms_layernorm(self.input_layernorm, hidden_states, gemma = True)
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states = hidden_states,
            causal_mask = causal_mask,
            attention_mask = attention_mask,
            position_ids = position_ids,
            past_key_value = past_key_value,
            output_attentions = output_attentions,
            use_cache = use_cache,
            padding_mask = padding_mask,
            **kwargs,
        )
        hidden_states = fast_rms_layernorm(self.post_attention_layernorm, hidden_states, gemma = True)
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = fast_rms_layernorm(
            self.pre_feedforward_layernorm, hidden_states, gemma = True
        )
        hidden_states = self.mlp(hidden_states)
        hidden_states = fast_rms_layernorm(
            self.post_feedforward_layernorm, hidden_states, gemma = True
        )
        hidden_states = residual + hidden_states

    outputs = (hidden_states,)
    if output_attentions:
        outputs += (self_attn_weights,)
    if use_cache:
        outputs += (present_key_value,)
    return outputs


from math import sqrt as math_sqrt

KV_CACHE_INCREMENT = 256  # KV Cache update size
torch_nn_functional_softmax = torch.nn.functional.softmax
torch_matmul = torch.matmul
torch_tanh = torch.tanh


def Gemma2Attention_fast_forward_inference(
    self,
    hidden_states: torch.Tensor,
    past_key_value: Optional[Tuple[torch.Tensor]],
    position_ids,
    do_prefill = False,
    attention_mask = None,
    use_sliding_window = False,
    **kwargs,
):
    Xn = hidden_states
    bsz, _, hd = hidden_states.size()
    K1, V1 = past_key_value
    dtype = Xn.dtype

    n_heads = self.config.num_attention_heads
    n_groups = self.num_key_value_groups
    n_kv_heads = self.config.num_key_value_heads
    head_dim = self.head_dim
    # assert(n_kv_heads * n_groups == n_heads)

    hidden_size = self.config.hidden_size
    attention_size = n_heads * head_dim
    seq_len = K1.shape[-2]
    kv_seq_len = seq_len + 1
    device = hidden_states.device

    # Prefill phase
    # if not hasattr(self, "paged_attention"):
    if do_prefill:
        self.paged_attention = torch.empty(
            (KV_CACHE_INCREMENT + seq_len + 1, 2, bsz, n_kv_heads, head_dim),
            dtype = dtype,
            device = device,
        )
        self.paged_attention_K = self.paged_attention[:, 0]
        self.paged_attention_V = self.paged_attention[:, 1]
        self.paged_attention_K[:seq_len] = K1.permute(2, 0, 1, 3)
        self.paged_attention_V[:seq_len] = V1.permute(2, 0, 1, 3)
        self.temp_QA = torch.empty((2, bsz, 1, attention_size), dtype = dtype, device = device)
        self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads * head_dim), dtype = dtype, device = device)
        self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = device)
        # Only for Gemma2
        self.temp_O = torch.empty((bsz, 1, hidden_size), dtype = dtype, device = device)
        self.attention = torch.empty(
            (bsz, n_heads, 1, KV_CACHE_INCREMENT + seq_len), dtype = dtype, device = device
        )

        # See https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
        # Gemma 9b should use 256 and not 224 (hs / nah). 27b uses the below
        # We default to using the config file itself
        # s = self.config.hidden_size // self.config.num_attention_heads
        self.scalar = 1.0 / math_sqrt(self.config.query_pre_attn_scalar)
        # self.scalar = 1.0 / math_sqrt(self.config.hidden_size // self.config.num_attention_heads)
        self.half_head_dim = head_dim // 2
        self.t = self.config.attn_logit_softcapping
        self.reciprocal_t = 1.0 / self.config.attn_logit_softcapping
    elif kv_seq_len >= self.paged_attention.shape[0]:
        self.paged_attention.resize_(
            (
                self.paged_attention.shape[0] + KV_CACHE_INCREMENT,
                2,
                bsz,
                n_kv_heads,
                head_dim,
            )
        )
        self.paged_attention_K = self.paged_attention[:, 0]
        self.paged_attention_V = self.paged_attention[:, 1]
        self.attention.resize_((bsz, n_heads, 1, self.attention.shape[-1] + KV_CACHE_INCREMENT))

    Qn = fast_linear_forward(self.q_proj, Xn, out = self.temp_QA[0])
    Kn = fast_linear_forward(self.k_proj, Xn, out = self.temp_KV[0])
    Vn = fast_linear_forward(self.v_proj, Xn, out = self.temp_KV[1])
    Qn = Qn.view(bsz, 1, n_heads, head_dim).transpose(1, 2)
    Kn = Kn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2)
    Vn = Vn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2)

    # cos, sin = self.rotary_emb(Vn, seq_len = kv_seq_len)
    # Qn, Kn = inplace_rope_embedding(Qn, Kn, cos, sin, position_ids)
    cos, sin = self.rotary_emb.get_cached(kv_seq_len, Qn.device.index)
    # Transformers 5.x: position_ids may be [batch, full_seq_len]; slice to last
    if position_ids.dim() >= 2 and position_ids.shape[-1] > 1:
        position_ids = position_ids[:, -1:]
    cos = cos[position_ids].unsqueeze(1)
    sin = sin[position_ids].unsqueeze(1)
    h = self.half_head_dim

    RH_Q = self.RH_Q
    RH_Q[:, :, :, :h] = Qn[:, :, :, h:]
    RH_Q[:, :, :, h:] = Qn[:, :, :, :h]
    RH_Q[:, :, :, :h].neg_()
    Qn *= cos
    Qn.addcmul_(RH_Q, sin)

    RH_K = RH_Q[
        :, :n_kv_heads, :, :
    ]  # torch.empty((n_kv_heads, 1, head_dim), dtype = dtype, device = "cuda:0")
    RH_K[:, :, :, :h] = Kn[:, :, :, h:]
    RH_K[:, :, :, h:] = Kn[:, :, :, :h]
    RH_K[:, :, :, :h].neg_()
    Kn *= cos
    Kn.addcmul_(RH_K, sin)

    # New KV cache
    # Kn = torch.cat([K1, Kn], dim = 2)
    # Vn = torch.cat([V1, Vn], dim = 2)
    self.paged_attention_K[seq_len] = Kn.permute(2, 0, 1, 3)
    self.paged_attention_V[seq_len] = Vn.permute(2, 0, 1, 3)
    Kn = self.paged_attention_K[:kv_seq_len].permute(1, 2, 0, 3)
    Vn = self.paged_attention_V[:kv_seq_len].permute(1, 2, 0, 3)

    # Handle sliding windows
    sliding_window = self.config.sliding_window
    if use_sliding_window and kv_seq_len > sliding_window:
        start = kv_seq_len - sliding_window
        Knn = Kn[:, :, start:, :]  # .contiguous()
        Vnn = Vn[:, :, start:, :]  # .contiguous()
    else:
        Knn, Vnn = Kn, Vn

    # Grouped query attention
    _, _, cached_len, _ = Knn.shape
    if n_groups != 1:
        Knn = Knn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)
        Vnn = Vnn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)
        Knn = Knn.reshape(bsz, n_heads, cached_len, head_dim)
        Vnn = Vnn.reshape(bsz, n_heads, cached_len, head_dim)

    # Attention
    # [TODO] Gemma2 uses manual matmul for all batch sizes since SDPA lacks
    # softcapping (tanh logit scaling). If PyTorch adds a softcap param to
    # SDPA, consider SDPA for bsz > 1 to match the llama/qwen3 pattern.
    Qn *= (
        self.scalar
    )  # See https://github.com/ggerganov/llama.cpp/issues/7805#issuecomment-2153349963
    # It seems like doing (Q * scalar) @ K is better than (Q @ K) * scalar to stop overflows
    A = torch_matmul(Qn, Knn.transpose(2, 3), out = self.attention[:, :, :, :cached_len])

    # Softcapping must happen BEFORE the mask is applied.
    # Reference: google-deepmind/gemma _modules.py and transformers gemma2 eager_attention_forward
    A *= self.reciprocal_t
    A.tanh_()
    A *= self.t  # Logit softcapping

    if attention_mask is not None and isinstance(attention_mask, torch.Tensor):
        # Slice mask to match K/V when sliding window is active
        if attention_mask.shape[-1] != A.shape[-1]:
            attention_mask = attention_mask[:, :, :, -A.shape[-1] :]
        A += attention_mask

    A[:] = torch_nn_functional_softmax(A, dim = -1, dtype = torch.float32)  # .to(A.dtype)
    A = torch_matmul(A, Vnn, out = Qn)
    A = A.transpose(1, 2)
    A = A.reshape(bsz, 1, attention_size)
    A = fast_linear_forward(self.o_proj, A, out = self.temp_O)
    return A, (Kn, Vn)


# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L825
# @torch.inference_mode
def Gemma2Model_fast_forward_inference(
    self,
    input_ids,
    past_key_values,
    position_ids,
    attention_mask = None,
    **kwargs,
):
    out_weights = tuple(
        torch.empty_like(
            self.model.layers[0].input_layernorm.weight,
            dtype = torch.float32,
            device = torch.device(x),
        )
        for x in range(DEVICE_COUNT)
    )
    input_ids = input_ids[:, : self.max_seq_length]
    hidden_states = self.model.embed_tokens(input_ids)
    hidden_states = hidden_states.to(_get_dtype(dtype_from_config(self.config)))
    # 3072**0.5 = 55.5000 in bfloat16, whilst 55.4256 in float32
    # 2048**0.5 = 45.2500 in bfloat16, whilst 45.2548 in float32
    hidden_states *= torch.tensor(math_sqrt(self.config.hidden_size), dtype = hidden_states.dtype)

    bsz, q_len, hd = hidden_states.shape
    seq_len = past_key_values[0][0].shape[-2]
    if bsz != 1:
        if HAS_FLASH_ATTENTION_SOFTCAPPING:
            SWA = True
            GA = False
        else:
            SWA = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (bsz, q_len),
                hidden_states,
                seq_len,
                sliding_window = self.config.sliding_window,
            )
            GA = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (bsz, q_len),
                hidden_states,
                seq_len,
            )
    else:
        SWA = attention_mask
        GA = attention_mask
    next_decoder_cache = []
    for idx, decoder_layer in enumerate(self.model.layers):
        # For pipeline parallelism, we need to move all tensors to the same device
        # note that this movement is once per GPU in PP
        device_index = getattr(decoder_layer, "_per_layer_device_index", 0)
        hidden_states, position_ids = move_to_device(device_index, hidden_states, position_ids)

        use_sliding_window = idx % 2 == 0

        residual = hidden_states
        hidden_states = fast_rms_layernorm_inference_gemma(
            decoder_layer.input_layernorm, hidden_states, out_weights[device_index]
        )
        hidden_states, present_key_value = Gemma2Attention_fast_forward_inference(
            decoder_layer.self_attn,
            hidden_states = hidden_states,
            past_key_value = past_key_values[idx],
            position_ids = position_ids,
            attention_mask = SWA if use_sliding_window else GA,
            do_prefill = not hasattr(decoder_layer.self_attn, "paged_attention"),
            use_sliding_window = use_sliding_window,
        )
        hidden_states = fast_rms_layernorm_inference_gemma(
            decoder_layer.post_attention_layernorm,
            hidden_states,
            out_weights[device_index],
        )
        hidden_states += residual

        residual = hidden_states
        hidden_states = fast_rms_layernorm_inference_gemma(
            decoder_layer.pre_feedforward_layernorm,
            hidden_states,
            out_weights[device_index],
        )
        hidden_states = fast_geglu_inference(decoder_layer.mlp, hidden_states)
        hidden_states = fast_rms_layernorm_inference_gemma(
            decoder_layer.post_feedforward_layernorm,
            hidden_states,
            out_weights[device_index],
        )
        hidden_states += residual

        next_decoder_cache.append(present_key_value)
    hidden_states = fast_rms_layernorm_inference_gemma(
        self.model.norm, hidden_states, out_weights[device_index]
    )

    return BaseModelOutputWithPast(
        last_hidden_state = hidden_states,
        past_key_values = next_decoder_cache,
        hidden_states = [],
        attentions = [],
    )


class FastGemma2Model(FastLlamaModel):
    @staticmethod
    def pre_patch():
        init_name, function = patch_linear_scaling(
            model_name = "gemma2",
            rope_module = GemmaFixedRotaryEmbedding,
            scaled_rope_module = GemmaFixedLinearScalingRotaryEmbedding,
            attention_module = Gemma2Attention,
        )
        if init_name is not None:
            exec(function, globals())
            Gemma2Attention.__init__ = eval(init_name)
        Gemma2Attention.forward = Gemma2Attention_fast_forward
        Gemma2SdpaAttention.forward = Gemma2Attention_fast_forward
        Gemma2FlashAttention2.forward = Gemma2Attention_fast_forward
        Gemma2DecoderLayer.forward = Gemma2DecoderLayer_fast_forward
        Gemma2Model.forward = LlamaModel_fast_forward
        Gemma2ForCausalLM.forward = CausalLM_fast_forward(Gemma2Model_fast_forward_inference)
        PeftModelForCausalLM.forward = PeftModel_fast_forward
        fix_prepare_inputs_for_generation(Gemma2ForCausalLM)

        # Solves https://github.com/unslothai/unsloth/issues/168
        # Static KV Cache was introduced in 4.38.0, causing training to be much slower.
        # Inference can now be CUDAGraphed, but we shall retain the old rotary embeddings.
        # https://github.com/huggingface/transformers/pull/27931
        # https://github.com/huggingface/transformers/blob/v4.37.2/src/transformers/models/llama/modeling_llama.py
        import transformers.models.gemma2.modeling_gemma2

        transformers.models.gemma2.modeling_gemma2.Gemma2RotaryEmbedding = GemmaFixedRotaryEmbedding
        return

    @staticmethod
    def post_patch(
        model,
        tokenizer,
        correct_dtype = None,
    ):
        # Gemma does not downcast RoPE
        model, tokenizer = patch_model_and_tokenizer(
            model, tokenizer, downcast_rope = False, correct_dtype = correct_dtype
        )

        # Add 1 to weight
        # return output * (1 + self.weight)
        # https://github.com/huggingface/transformers/blob/main/src/transformers/models/gemma/modeling_gemma.py#L89
        from transformers.models.gemma2.modeling_gemma2 import Gemma2RMSNorm

        # Freeze all parameters except LoRA
        # We do this first since += 1 seems to not be liked by requires_grad = True
        for name, param in model.named_parameters():
            if ".lora_A." in name or ".lora_B." in name:
                param.requires_grad_(True)
            else:
                param.requires_grad_(False)

        # Patch RMS Layernorm
        for name, module in model.named_modules():
            if isinstance(module, Gemma2RMSNorm):
                # Must be in float32
                # https://github.com/keras-team/keras-nlp/blob/v0.8.2/keras_nlp/models/gemma/rms_normalization.py#L36
                # module = module.to(torch.float32)
                # Leave + 1 to Triton kernel itself
                # module.weight += 1.0 # return output * (1 + self.weight)
                if not hasattr(module, "variance_epsilon"):
                    module.variance_epsilon = module.eps  # Gemma doesn't use variance_epsilon

        # Clear deleted GPU items
        import gc

        for _ in range(3):
            gc.collect()
            torch.cuda.empty_cache()
        return model, tokenizer
